System and method for wireless location performance prediction

ABSTRACT

The software of the present invention predicts the performance of a wireless location system, including its accuracy, availability, and coverage. The tool can determine if the deployed location sensors meet, exceed, or fall short of providing the expected coverage and performance. The tool allows the location system designer to eliminate redundancies if not all sensors are needed and propose additional sites where coverage holes are present. The software tool includes a graphical user interface for ease of use.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is a continuation of U.S. patent applicationSer. No. 09/848,052, filed May 3, 2001, which claims the benefit of thefiling date of U.S. Provisional Patent Application Ser. No. 60/202,147,filed May 5, 2000 and entitled “PERFORMANCE ANALYSIS TOOL FOR LOCATIONSYSTEMS”, the entire contents of which are hereby expressly incorporatedby reference.

FIELD OF THE INVENTION

The present invention relates to a system and method for wirelesslocation systems. More specifically, the invention relates to aperformance analysis software tool designed to predict the performanceand geographical coverage of wireless location systems.

BACKGROUND OF THE INVENTION

Various location determining systems (LDS) are used to determine thelocation of a mobile user. For example, a Global Positioning System(GPS) typically uses a set of twenty-four orbiting satellites to allowground-based users to determine their locations. These systems providethe user with location information based on LDS such as GPS data. Somelocation systems include LDS elements integrated into a cellular phone,while others have equipment added to the wireless infrastructure.

Designing a location system has been cumbersome and involvesmanipulation and analysis of a variety of information. Location sensordensity and geometry are extremely important to obtaining acceptablelocation data. For example, Angle of Arrival (AOA) techniques requiresensor information from a minimum of two sites to obtain a location,three to estimate the quality of a location and a minimum of four toidentify and reject severely corrupted (multipath) data from one site.Time Difference of Arrival (TDOA) techniques (both at the sites and inthe handsets) require sensor information from a minimum of three, four,and five sites for the same capabilities. Factors such as the type ofservice area to be covered (rural vs. urban) or the characteristics ofthe wireless network (existing cell site densities, geometry of cellsites with respect to each other and areas to be covered, restrictionson antenna placement, availability at cell sites, etc.) are all factorsaffecting location performance and are incorporated in this softwareplatform.

The geometry of the site infrastructure has a major impact on thequality of the locations. Geometric Dilution Of Precision (GDOP) playsan important role which must be considered. An extreme example of poorgeometry is found along (relatively) straight highways between majorcities. In these cases, cell sites are often located in a string nearthe highways providing cellular/PCS coverage only to the highway. An AOAlocation system with sensors located only at the sites will only be ableto locate a mobile set as being between two highway sites. TDOA systemswill only be able to locate the mobile set along a hyperbolaintersecting the highway. This is at least better information if one canassume that the mobile set is on the highway and not on a nearbyfarm-to- market road. Even this would require a unique algorithm for useonly in these areas. Note that a combined AOA/TDOA system would be ableto provide location services under these circumstances.

Location systems have coverage requirements that conflict with those ofCellular/PCS networks. For example, an objective of a cellular/PCSdesign is to limit the radio coverage of a given base station. Alocation system, on the other hand, requires that each receiver site“see” (i.e., receive a useful signal) well beyond the limits of a singlebase station. A location system or technique generally operates thebest, i.e., it offers the best accuracy for the highest percentage ofthe time, when it has an abundance of sites that receive the signal fromthe phone. This means that the higher the number of receiver sites that“see” the mobile unit the better the performance.

As explained above, because of the divergent requirements of wirelesscommunication and wireless location systems, a specialized design andanalysis software tool is required for proper design of a locationsystem. There are a number of Cellular/PCS coverage design toolsavailable on the market but none provide the utility to predict alocation system coverage.

Therefore, there is a need for a software tool for analyzing wirelesslocation systems with a user friendly graphical user interface (GUI).

SUMMARY OF THE INVENTION

The software of the present invention predicts the availability andaccuracy of locations determined by a variety of different techniquessuch as Angle of Arrival (AOA), Time of Arrival (TOA), Time Differenceof Arrival (TDOA), and hybrid variations of these angle and time ofarrival as well as signal strength based techniques. The tool is capableof performance analysis whether the pertinent measurements are performedin fixed sensors associated with the network infrastructure, or in thehandset. The tool can also determine if the deployed location sensorsmeet, exceed, or fall short of providing the expected coverage andperformance. The tool allows the location system designer to eliminateredundancies if not all sensors are needed and propose additional siteswhere location coverage holes are present. The software tool offerssimilar capabilities to location and monitoring services (LMS) networks.

In one aspect the present invention describes a method for analyzingperformance of a wireless location system. The method includes the stepsof storing data related to location equipment, wireless infrastructure,handsets, terrain map, and morphology map; generating a site radial filefor path loss and time/angle error based on the stored terrain andmorphology maps; computing a multi-site forward and reverse link signalstrength map for determining coverage of the location system; generatinga multi-site margin/error map from the computed multi-site forward andreverse link signal strength map and the stored data; and generating anerror estimate map for the location system.

In another aspect, the present invention discloses a system forperformance analysis of a location system comprising: means forgenerating a radial model and a radial map including a plurality ofradial paths for a site from a stored raster map; means for selecting apropagation model from a stored plurality of propagation models forpredicting a path loss along each radial path; at each point along aradial path, means for predicting accumulated angular errors and timedelay errors; and means for generating an error estimate from the pathloss and the accumulated angular errors and time delay errors.

BRIEF DESCRIPTION OF THE DRAWINGS

The objects, advantages and features of this invention will become moreapparent from a consideration of the following detailed description andthe drawings, in which:

FIG. 1 is a simplified process flow, according to one embodiment of thepresent invention;

FIG. 2 is a simplified process flow related to FIG. 1, according to oneembodiment of the present invention;

FIG. 3 is a picture providing an example screen of a GUI, according toone embodiment of the present invention;

FIG. 4 is an exemplary window within the GUI of FIG. 3;

FIG. 5 is a simplified process flow for generating site radial maps forterrain and land use, according to one embodiment of the presentinvention;

FIG. 6 is a simplified process flow for generating site radial maps forpath loss and time/angle error, according to one embodiment of thepresent invention;

FIG. 7 is a simplified process flow for generating rectangular maps fromradial maps, according to one embodiment of the present invention;

FIG. 8 is a simplified process flow for generating a cluster map,according to one embodiment of the present invention;

FIG. 9 is a simplified process flow for generating forward and reverselink signal strengths, according to one embodiment of the presentinvention;

FIG. 10 is a simplified process flow for generating mobile unit transmitpower, according to one embodiment of the present invention;

FIG. 11 is a simplified process flow for generating a multi-sitetime/angle error map, according to one embodiment of the presentinvention;

FIG. 12 is a simplified process flow for generating a location errormap, according to one embodiment of the present invention;

FIG. 13 is an exemplary picture showing location error in a metropolitanarea, according to one embodiment of the present invention;

FIG. 14 is an exemplary picture showing number of location sensorsreceiving useful signal from a handset, according to one embodiment ofthe present invention; and

FIG. 15 is an exemplary block diagram of a software tool, according toone embodiment of the present invention.

DETAILED DESCRIPTION

The present invention is a performance analysis software tool designedto predict the performance and geographical coverage of a wide array ofwireless location systems. The tool applies to both network-based andhandset based location technologies. These location techniques findbroad application in Cellular and PCS networks, as well as in otherwireless networks, including those for LMS. The software tool of thepresent invention is designed to work as a stand alone package or as avalue added adjunct to wireless communication network design tools.

In one embodiment, the software tool of the present invention provides awindows based user interface that is fairly simple to use by a radio ordesign engineer. It also provides outputs in the forms of graphs, tablesand reports for display and/or printing. In one embodiment, the toolruns on a Pentium-based, IBM-PC compatible machine running Windows NT4.0. However, hosting on other platforms with a variety of differentoperating systems such as Linux or UNIX are also provided. The tool ismodular in its structure to allow the gradual inclusion of capabilitiesand features, as well as to support on-going refinements. The typicaluser does not need to perform any programming, although hooks are madeavailable to add modules by authorized technical users. Such developmentcould be initiated based on the users' unique needs and requests.

The accuracy of the location determination is described by the expectedlocation determination error (based on the mean square error). There aretwo widely used definitions for availability, and both capabilities areprovided by the tool. In one, availability is determined by whetherlocation determination is feasible at all or not. This relates directlyto the minimum number of location sites (sensors) that “cover” aspecific point under consideration on the map. (The minimum number ofsites required to yield a location varies from one technique to anotherand is described briefly below for the different techniques.) In thesecond definition, availability is whether a location systemstatistically provides a location accuracy that meets a pre-selectedaccuracy threshold for example, 100 m over 67% of a given area.

In the family of network or infrastructure based location techniques,the position determination is performed by means of sensors that areplaced at fixed locations, most typically co-located with the wirelesscell sites.

For example, AOA sensors include specially designed antennas mounted atcell sites or other propitious locations to measure the angle of arrivalof the mobile signal. Because the wave front arrives at the differentlypositioned antenna elements at slightly different times, the phasemeasured at each element relative to the others is different. Angle ofarrival is calculated from these differing phase measurements using aspecially designed receiver and is delivered to the location determiningsystem controlling element as azimuth from true north (or other fixeddirectional reference).

The intersection of the rays formed by the reported azimuths provide thelocation of the caller. A major advantage of AOA position finding isthat only two sites are required to obtain a position. Continuous systemcalibration is also unnecessary. AOA systems, however, are particularlysensitive to wave reflections caused by multipath in the urbanenvironment. Their accuracy degrades also as the distance between thetransmitter (e.g., the handset) and the AOA sensor increases.

In TDOA, the difference in the arrival times at multiple receiver sitesof a signal emitted from a transmitter is used to calculate the positionof that transmitter. An advantage of the TDOA approach is that specialantennas are not required—current site antennas may be used. The sensortypically contains the functions of reception (filtering,down-conversion), signal sampling and storage, demodulation of certainsignals, and calibration. Continuous calibration or control of systemtiming accurate to 10's of nanoseconds is required to achieve therequired time measurement accuracy.

A closely related technique to TDOA is TOA, which applies when thetransmitter and receiver are tightly time synchronized. In this case,the differential time alignment in TDOA is not required, and it ispossible to measure the round trip propagation delay between a sensorand a handset, hence infer the range (distance) to the handset. TOA isnormally used in conjunction with AOA or TDOA to broaden theirapplicability or enhance performance.

In hybrid angle and time techniques, the location system attempts tocombine the performance advantages of AOA with those of TOA or TDOA,enabling, in theory, using only one or two sensors to detect theposition of the transmitter (e.g., the caller's handset), even when poorgeometry renders pure AOA systems ineffective. These hybrid systems alsopromise improved performance (location accuracy and coverage). Thedrawback is the increased complexity at each hybrid site and in systemcontrol.

Multipath pattern recognition is an approach which is not immediatelyrelated to TDOA, however it is often combined with AOA to improve itsperformance. Multipath pattern recognition entails comparing thesignature of the signal received at various sensor sites with the thatstored in a substantial data base containing the signatures createdduring extensive calibration runs spanning the area. Pattern recognitionand classification algorithms are used to obtain the best match and thelocation. This technique is better suited to long calls or mobile callswhere a significant amount of filtering can be applied to discarderroneous matches.

In handset based approaches, the handset plays an active role inperforming measurements and optionally computing the location. Systemspertaining to this family can be divided into the following three broadclasses.

Enhanced Observed Time Difference (E-OTD) technique is essentially TDOAbut with the measurements performed at the handset. The times of arrivalof signals from the serving as well as neighboring cell sites areobserved at the handset. This technique also entails the broadcast bythe network of the differences between the actual time bases at thedifferent cell sites in the area. This information is used by thehandset to enhance the location computation if it is performed there.Alternately, the information on the OTD measurements performed at thehandset could be transmitted back to the network where the location iscomputed. In either case, from a location determination stand point, thetechnique is similar in its performance to TDOA, but with distinctparameters relating to its implementation particulars. E-OTD has beenthe technique of choice for a number of GSM operators and infrastructurevendors.

Forward Link Triangulation (FLT) technique is essentially TDOA at thehandset. This flavor of TDOA is most commonly applied to CDMA networksbecause of the tight timing constraints maintained on the pilottransmitted from each CDMA cell site. FLT generally uses these pilotsfrom the serving and neighboring sites to perform a TDOA computation atthe handset. Analogous to E-OTD, some calibration of the time baseaccuracies at the cell sites is performed and is disseminated over theair to the handsets.

GPS Based Location techniques rely on the presence of a GPS receiver orsensor in the handset. Pseudo ranges from GPS satellites are measuredand used to obtain the location. The computation is performed either inthe handset or at a server on the network side if the measurementinformation is transmitted back to the network. So called assistancedata may also be transmitted from the network to aid the performance ofthe GPS measurements performed in the handset.

The tool of the present invention obtains the geographic coverage andperformance of AOA and TDOA based location systems including theirvariations and hybrids, whether the measurements are performed at fixedsensor sites or at the handset. As such, traditional network-based AOAand TDOA, network-based hybrid AOA/TDOA, and E-OTD and FLT are alltechniques whose performance is predicted by the tool. Techniques thatapply to a CDMA network, such as FLT, require special handling in theprogram to account for CDMA's unique radio coverage prediction, but asfar as location performance prediction, the same procedures andalgorithms described in detail below are applied.

Although the detailed algorithms discussed above do not explicitlydepict the case of GPS measurement in the handset, because GPS is a TDOAsystem with the transmitters in the sky, the same methodology describedbelow is readily applied to predict the performance of those systems aswell. Straight-forward extensions to the propagation, geometry andsignal models used with terrestrial transmitters are applied inobtaining the performance for the GPS case.

FIG. 15 is an exemplary simplified block diagram of some componentsincluded in one embodiment of the tool of the present invention. Asshown in FIG. 15, in this embodiment, the software tool of the presentinvention includes the following components: infrastructure technologiesand environment databases 1502, model & algorithm packages 1504, andinterfaces 1506. Databases 1502 includes wireless infrastructuredatabases 1508, location system deployment databases 1510, geographicdatabases, and databases for multipath profiles 1524. geographicdatabases include information for: terrain 1518, morphology/land use1514, roads 1516, salient features 1520 (e.g., large towers orobstacles), and population 1522.

Model & algorithm packages 1504 include multipath profilegeneration/characterization algorithms and multipath profile databases1530, location technology databases and location system performancemodels 1532, propagation algorithm databases 1526, and reverse linkadjustments 1528.

Interfaces 1506 include GUI 1538, printing module & interface 1540, LANinterface 1542, and outside system interface 1544. System administrationutility module 1534 handles the system functions controlled by a systemadministrator, such as system access control using a password system.System Controller 1536 provide a variety of system control functions.

In one embodiment, the software tool supports the entry, reading, orimporting of the specifics of a target cellular or PCS infrastructure.This wireless infrastructure data includes:

-   -   air interface type; cell site locations (latitude and        longitude); site elevation AMSL (maybe computed automatically        from terrain); sector height (above surrounding terrain); number        of sectors; antenna gain; TX and RX pattern propagation model        type; downtilt; number of channels; transmit powers; and power        control window upper and lower limits.

The default mode of data entry is the keyboard/GUI. However, other modesof data entry are possible. In one embodiment, each cell site of aninfrastructure is assigned a number and each of its sectors is assignedanother number to distinguish antennas that may be physically separate(e.g., on different sides of a building). In one embodiment, the toolincludes separate databases for each target cellular/PCS infrastructure.

In one embodiment, the software tool supports entering, reading, orimporting of the location system infrastructure deployment information.The information pertaining to the location system deployment includes:location system type or name; unit type (if multiple receiver types orconfigurations are available); location receivers' antenna category(same as wireless network or not); location system antenna locations(latitude and longitude or cell site number if same antenna); antennatype (if not same as cellular); number of antenna units at a giveninstallation; location system antenna elevation; location system antennaheight; and cabling losses. In one embodiment, the tool includesseparate databases for each location system deployment. Informationpertaining to a given location technology that is not deployment(placement) specific is maintained in another database called theLocation Technologies database.

In one embodiment, the tool reads and maintains database with parametersspecific to various location technologies. Each databases containsinformation specific to one location technology or one release versionof it that is under investigation. This non-placement specificinformation is retained in the location infrastructure database. Some ofthese parameters include: type of technology; antenna types (gainsand/or patterns); receivers' sensitivities and noise data; receiverbandwidth; integration time(s); known receiver biases; any known orestimated receiver jitter; quality indicators of receiver or receivertype; and quality indicators computation (if available).

The tool contains location system performance algorithm packages toenable predicting system performance. For every location technologyidentified above, performance computation algorithms are developed usingboth theoretical and empirical formulas. Distinct modules within eachpackage are possible to compute the effects of specific phenomena on thesubject technology, for example, GDOP. The user is able to adjustcertain parameters in the equations to support “what if” and sensitivityanalyses. These algorithms typically take inputs from multipledatabases, including: infrastructure, geographic, multipath profile, andlocation technology. They also exercise or cause the execution of thepropagation package and wireless control algorithms. The outputs of theperformance algorithms are expressed in several ways, including: averageand RMS errors, probability of missed detection, number and identity oflocation receivers observing a mobile at given thresholds, and coverageavailability (assuming these thresholds). In one embodiment, the resultsare made available in tabular formats, graphical formats using datalayers, and in summary reports.

The tool also supports the entry, reading, or importing of the specificsof mobile units. This data is primarily the unit type and model. For anumber of models, default characteristics are initially read and thenmaintained into the database. The characteristics include: peak transmitpower; power control range; support of discontinuous transmission;speech specification (analog or speech coding rate); and support of dataservices, if any.

The tool has the ability to read and store the terrain information for acertain area determined by its corner latitude and longitudecoordinates. The tool is also able to display this information as araster data layer. Maps from the USGS or other private sourcesconforming to standard formats are supported. For example, both onedegree and 7.5 minute arc maps are supported. 300 m and 100 m terrainmaps, among others, are also supported. The tool also has the ability toread, store and display morphology type maps. One such source is theUSGS Land Use Land Cover (LULC) data maps. The tool also makes itpossible to edit and design custom morphology maps. It displays thisinformation as a raster data layer.

The GUI allows simple entry of many information elements throughdialogue windows. However, some elements may also be obtainable fromsources other than the GUI, e.g., an external system like a switch or aCD-ROM. Preferably, each database has a set of menu-driven dialoguewindows that enable entering and/or editing, as appropriate, theirinformation elements. For example, nominal transmit power, receiver sitecoordinates, antenna height, receiver noise figure, land use/morphologycategory, and so on. The dialogue windows enable the creation of a newwireless or location site and the entry of its parameters into theappropriate databases. Preferably, each model within a package hasassociated with it a dialogue window to specify, enter parameter into,or edit (as appropriate) the model. Certain core parts of the modelsrequire administrator access for modification.

Prior to performance prediction, two distinct types of site sectors aredefined in a project within the tool: (1) wireless sectors (e.g.,Cellular, PCS, ESMR), and (2) geolocation (or simply location) sectors.Sites may be comprised of either wireless sectors, geolocation sectors,or both. A unique graphical user interface (GUI) allows the user todefine the project, enter and edit the particulars of both the wirelessand location sites, and otherwise input and manipulate all theinformation that the program may need to provide the predictions soughtby the user. A picture providing an example screen of this GUI is shownin FIG. 3. An exemplary window within this layered GUI, called the SiteEditor, is shown in FIG. 4. The Site Editor provides a mechanism for theuser to input, edit and select wireless and geolocation siteinformation.

Furthermore, the tool is able to read, store and display interstate,major and secondary roads. This data may be stored as line or curverather than raster data. It is possible to display and/or overlay thisinformation with/on other data layers. The tool is capable ofdistinguishing between Interstate highways and other roads. The toolreads, maintains and displays population density raster maps. In oneembodiment, data based on US census information is used. Also, it ispossible to read or define information elements that specify certainsalient features in the area under investigation. Examples include largetransmitters, tanks, obstructions, airports, etc.

The software tool of the present invention begins with wireless (e.g.,cellular) site or area coverage prediction methods, and addsconsiderable new modeling to arrive at a prediction of geolocationnetwork performance. An exemplary process is illustrated at a top levelin the flow charts depicted in FIG. 1 and FIG. 2. These charts showeight major “boxes” or steps culminating in obtaining the desiredlocation error map. The description below will follow these top levelcharts and will elaborate on the eight main steps, providing details ontheir algorithmic contents with a more detailed flow chart for eachstep.

FIGS. 1 and 2 are exemplary process flows according to one embodiment ofthe present invention. In block 102 of FIG. 1, site radial maps forterrain and land use are generated. This entails developing a radialmodel and a radial map, centered on the site, for the terrain andmorphology (land use) from common raster maps. The details of thisprocess are shown in the exemplary flow process of FIG. 5.

At each point along a radial path, a combination of accumulated angularerrors and time delay errors (for AOA, TDOA, their hybrid locationtechniques) are predicted. The combined path loss and accumulated errorradial maps are then converted to square raster maps, one for eachcellular site and each geolocation site, as shown in block 104. Thedetails of this process are shown in the exemplary flow processes ofFIGS. 6 and 7.

A cluster of sites of fairly arbitrary size is also defined. The mapscalculated for the individual sites are then combined into a single,combined raster map for the cluster in block 106. These maps contain ateach point, the path loss for the best wireless server and the errordata for the geolocation sites with the highest received signals. Up toN geolocation sites can be included, where N is currently 8 by defaultbut can be changed. Details of the steps of block 106 are furtherdescribed in FIG. 8.

In block 108 (explained further in FIG. 9), both forward and reverselink signal strength maps are computed for the cellular network todetermine the presence of cellular coverage. From these maps, a map ofactual cell phone transmit power is calculated. The receive powermargins are then computed for the geolocation sites (up to N asdescribed above) in block 202 (explained further in FIG. 10).

In block 204, a multi-site receive power map, containing the signalmargins at each map point, is then constructed for these location sites.The additional angle and/or delay noise at each point due to geolocationsensors receive noise are then estimated. These errors are combined withthe noise previously estimated from the terrain/land use environment andalready available in the cluster raster map in block 206 (explainedfurther in FIG. 11).

In block 208, at each map point, an error covariance matrix is thengenerated from the up to N angle and/or time error estimates. Thesemi-major axis of the error ellipse is derived from this matrix, todetermine the error estimate, as shown in block 210 (explained furtherin FIG. 12).

The error results are then output in the form of a display map coveringthe cluster or metropolitan area. Color coding is keyed to the size ofthe estimated error. Alternately, the estimated probability that theerror will meet a specified criterion is displayed. The tool isinteractive in nature and allows the user to conduct a number of what-ifscenarios, to optimize location site placement and location systemperformance.

Referring now to FIG. 5, the number of radials required to adequatelyrepresent the site's signal propagation is calculated in block 502. Thisnumber is based on the resolution of the original terrain file data andthe (entered) calculation radius for the site. The resolution along eachradial is typically the same as the resolution of the terrain file data.Next, the latitude and longitude are calculated in block 512 for eachpoint (block 508) on each radial (block 504). This requires the sine andcosine of the azimuth of the radial (block 506 to calculate thehorizontal and vertical distances from the site (center) to the point onthe radial, shown in block 510.

From these distances, the local radius of the earth, and the site'scoordinates, the longitude and latitude of a point on the radial aredetermined in block 512. The terrain altitude for the point (defined byits latitude and longitude) is obtained in block 514 from the originalterrain file 516, and a morphology code is obtained in block 520 fromthe original morphology files 518. The morphology code is an index intoa table that contains an effective height, loss, and effectiveobstruction width for each type of land usage (urban, light suburban,forest, open land, etc.). This information is then stored together inradial format. This process is repeated for the next point (blocks 522and 524) and the next radial (blocks 526 and 528).

The second major step for the top level exemplary process flow of FIG. 1is to generate the radial files for path loss and for the time and/orangle error, as shown in block 104. In one embodiment, the tool includesa set of propagation models that can be selected by the user for thepurpose of computing the path loss. The selection may be based on asector, or larger areas. The following models are some examples of thepropagation model selections: Okumura-Hata (cellular band), Cost 231(PCS band), Line of Site, Lee's model with effective antenna height,Fresnel zone corrections for paths partially obstructed by terrain.Other propagation models may be easily added to the tool. The user hasthe ability to override key default parameter values in the modelselected.; for example, the intercept of the Hata model (as seen on alog-log scale).

Furthermore, the design of the propagation module enables importingmeasurement data in a standard file format to perform least square fittype computations. The results of these computations are used to adjustthe parameters of the selected propagation model over a certainapplication region to be defined by the user. Another capability of thetool is to automatically select a permissible combination of models(e.g., O-H and LOS) on a per-pixel, per site basis. For each propagationmodel made available on the forward wireless link, a set of adjustmentsare implemented to allow its use for predicting the reverse link pathloss. The details of this multi-faceted process are depicted in theexemplary process flow of FIG. 6.

Referring now to FIG. 6, several salient sub-steps are shown including:spherical (4/3) earth computation (block 606); propagation modelcomputation to generate the path loss including the effects ofdiffraction and antenna height (blocks 628 and 630); computation of lossdue to antenna pattern (block 638); computation of the angle errors(block 662) or time errors (block 660) that result along the radialpaths in an AOA or TDOA based system, respectively.

For each entry along each radial route, the total number of obstructionsis determined and the characteristics of each path leg from site toobstruction to other obstruction(s) to mobile unit are determined inblock 620, and the accumulated diffraction loss is calculated for eachobstruction in block 628. Diffraction loss is calculated similar to themethod described in “The Mobile Radio Propagation Channel, Parsons,Halsted Press, 1992, pp 48-49,” the contents of which are expresslyincorporated by reference herein, following the “The Epstein-Petersonmethod”. For each point (block 616), the number of path legs areobtained in block 620. For each path leg (block 626), the diffractionloss is calculated and summed, as shown in block 628. From the last pathleg, and the average slope of the land just before the mobile unit, theeffective height of the site antenna is calculated in block 630. Boththe ray from the mobile and the slope of the land just before the mobileare projected back along the last leg to the site antenna position andthe difference in altitude is used as the effective antenna height. Fromthis and the total distance to the mobile unit, the nominal path loss isdetermined in block 632 from a selected propagation model 634 (e.g.Hata, Cost 231).

A host of standard propagation models are made available to the user topredict the path loss along each radial. Hybrid variations of thesemodels are also permissible in the tool. For example, Okumura-Hata (O-H)is one typical model widely used for urban or suburban propagation. TheO-H model includes parameters that could be adjusted and selected by anexpert user to adapt it specifically to certain propagationenvironments, e.g., an unusually open area. The tool's user friendly GUIpermits the user to select and edit these detailed parameters of thepropagation model. Other models that are more appropriate forspecialized propagation environments, e.g., for very high sites, can beused, at the user's option, instead of a standard O-H model.

This propagation model is then modified by the morphology loss at allpoints where the ray penetrates the morphology. Using the azimuth of thecurrent radial and the elevation to mobile unit or first obstruction, ifany; the stored antenna pattern 636; and the antenna's azimuth and tilt,the antenna pattern loss is determined in block 638. This loss is addedfor the total modeled path loss in block 640.

At each point along a radial path (blocks 644 and 646), a combination ofaccumulated angular errors or time delay errors are computed asapplicable (for AOA, TDOA, and hybrid location techniques). The angularmeasurement error is accumulated along the propagation path below themorphology (clutter) height based on an equivalent obstruction size, anequivalent obstruction density, and the distance from the sensorantenna. The steps in blocks 648 and 650 are used to determine whetheror not each of the rays is impacted by the clutter. The type of thealgorithm to be used for the analysis is determined in blocks 652 and654.

In block 656, individual AOA errors are calculated by calculating theangular error from the antenna caused by the path diffracting around theobstruction (or reflecting off an adjacent obstruction). In block 658,TDOA errors are calculated by subtracting the direct path from the sitefrom the path around the obstruction to the mobile unit. Each error issquared for accumulation as a variance in blocks 660 for TDOA/EOTD andblock 662 for AOA.

The equivalent obstruction sizes and densities are abstract termsarrived at through integrating field measurements into the model and aredifferent for each morphology (land use) type. The resolution along theradial path remains consistent with the terrain/morphology database.Next point and next leg is selected in blocks 664 and 668, respectively.If the end of loop is not reached (block 676), next point is selected inblock 616. If the end of loop is reached, next radial is selected foranalysis in block 610.

The next major step in FIG. 1 is to convert the combined path loss andaccumulated error radial maps to square raster maps, one for eachcellular site and each geolocation site. In one embodiment, the detailsof these conversions are performed as shown in FIG. 7. In FIG. 7, first,the box map dimensions are determined to fit the radial signal file andthe box map is set to the same resolution as the radial distanceresolution, as shown in block 702. From here, a signal map entry isobtained (block 708) for each latitude and longitude (blocks 704 and706) in the box map. The signal data (path loss and error) is thenstored in the box map's raster format, as shown in block 712.

It is quite common for radio engineering and location system planners tofocus their analysis on a subset of the sites that have been initiallyentered into the project prior to processing. This is, for example, toexamine in more detail a specific area or section of a city, or toconduct what-if analyses. The cluster size is fairly arbitrary. Therectangular maps calculated in the previous step for the individualsites are now combined into a single, combined raster map for thecluster. The details of this procedure are depicted in the exemplaryprocess flow of FIG. 8. The cluster maps contain at each point the pathloss for up to N sites as their coverage usually overlaps.

Referring to FIG. 8, based on the user's input, the boundaries (latitudeand longitude) of the overall cluster are determined from the site boxmap 805 sizes and positions, as shown in block 802. Then, the box map isaligned with the cluster map in box loop of block 804. This is done byobtaining the box map's upper left coordinate (latitude and longitude)in block 806 and determining where this position is in the Cluster map,as shown in block 808. Inserting the box data into the cluster mapstarts at this point. At each point in the cluster map (blocks 820 and822), the signal is extracted from the box map 805 in block 824 and isinserted into the cluster map in block 828. In this process, the sitesignals are ordered by received signal strength, the best wirelessserver being first in the list. These are the sites with the highestsignal levels received from the handset (or vise-versa at the handset).The default value for the number of sites N is 8, but can be changed andselected differently by a user. The resolution of the cluster maps isuser selectable but is typically the same as the original terrain maps.

Referring back to the high level exemplary process flow of FIG. 1, thenext step is computing the forward and reverse link signal strength mapsfor the best server in the cellular network, as shown in block 108. Thisis to determine the presence of cellular coverage. (This implies thatthe tool also determines the likely/best server for a given mobile'slocation.)

As shown in FIG. 9, for each line in the cluster map (block 902) asignal strength is obtained. The path loss is subtracted from theeffective radiated power (ERP) of the best server site to obtain theforward signal strength. Alternatively, the path loss is subtracted fromthe maximum ERP of the mobile unit to obtain the reverse signalstrength, as shown in block 912. The ERP of the cellular sites areobtained from the Site Data database 908 and the maximum ERP of themobile unit from the mobile unit data database 910.

Full use of most of the propagation models for path loss computationrequires the availability of terrain information. Nevertheless, In oneembodiment, the tool has two modes: a no terrain mode and a full terrainmode. When no terrain information is available the user enters a heightfor the mobile manually. The overall heights of the cell sites iscomputed from manual input of site elevation AMSL plus manual input ofan antenna height. When terrain data is available and accessible, thetool automatically computes the site and mobile user elevation from thecoordinates manually provided and the antenna height entered.

From the forward and reverse link signal maps (block 108), a map ofactual cell phone transmit power is calculated in block 202. The detailsof this steps are shown in FIG. 10. For each cluster line (block 1010)and cluster column (block 1012), forward signal margins and returnsignal margins are calculated in blocks 1024 and 1028, respectively.From the forward signal map 1014 and mobile unit data 1016, path lossand mobile unit receive sensitivity is subtracted from the ERP of thebest server site to obtain forward signal margin, as shown in block1024. If this margin is positive (block 1026), from return signal map1018 and site data 1020, path loss and site receive sensitivity issubtracted from the ERP of the mobile unit to obtain the return signalmargin, as shown in block 1028.

In block 1030, the mobile unit power is calculated as the minimum powernecessary to maintain the reverse link so long as both the forward andreverse margins are positive. Mobile unit power never goes below theminimum mobile unit power entered into the program. The computationtakes into account the power control implemented in the wireless networkand followed by the handset. Again, the particular parameters of themobile unit are obtained from the mobile unit data database 1022.

As depicted in block 204 of FIG. 2, the receiver powers are nowcomputed. The receiver power margins are also computed as described inthe previous paragraph for the pertinent geolocation sites (up to N asdescribed above). As shown in block 206, a multi-site margin/error map,containing the signal margins at each map point, is then constructed forthe N location sites. This map is essentially identical to the clustermap discussed previously, except that the location sensor sites are usedinstead of the cell sites and the error data is recorded in addition tothe signal margin data. Using algorithm database 126 the propagationalgorithms 128, and the time/angle error algorithms 130, a computationis performed at this stage to include the additional angle and/or delaynoise at each point due to geolocation sensors receive noise. These arebased on sensor characteristics and signal margins. The details forthese algorithmic steps are shown in FIG. 11.

Similar to the exemplary process of FIG. 8, the boundaries (latitude andlongitude) of the overall cluster are determined from the site box mapsizes and positions using the location signal box maps 1104, as shown inblock 1102 of FIG. 11. Then, the box maps within the overall cluster mapare aligned with it. This is done by obtaining the box map's upper leftcoordinate (latitude and longitude) in blocks 1110 and 1114. Next, foreach location in each box map (block 1126 and 1128), latitude andlongitude are determined in block 1130. The latitude and longitude arethen converted to the line/column coordinates used in the mobile unitpower map, as shown in block 1132. The mobile unit power is thenobtained in block 1133, similar to the process of FIG. 10.

Depending on whether the mobile unit is transmitting (because it haspositive cellular link margins), then the signal margin to the locationsensor (forward or reverse) is determined in blocks 1158 or 1154,respectively. If the margin is positive, then the appropriate error isadded to the position by insertion sort in blocks 1168 or 1166 dependingon the type of the location algorithm used (blocks 1162 and 1164). Theselocation sensor related errors are combined with the errors previouslyestimated from the terrain/land use environment and already available inthe cluster raster map.

The final computational step in FIG. 2 is to obtain at each map point anerror covariance matrix from up to N angle and/or time error estimates,as shown in block 208. The semi-major axis of the error ellipse isderived from this matrix. This is the error estimate at any given pointon the map. The detail of this step is shown in exemplary process flowof FIG. 12.

Referring now to FIG. 12, for each line and each column (blocks 1202 and1204), using the Multi-site Error map 1210, the inverse of thecovariance matrix is calculated in block 1208. The inverted matrix 1212is then used to calculate the semi-major axis of the error ellipse inblock 1214.

The covariance matrix for AOA is: $P = {\begin{bmatrix}{\sum\left( \frac{{- \Delta}\quad y_{k}}{d_{k}*\upsilon_{k}} \right)^{2}} & {\sum{\left( \frac{{- \Delta}\quad y_{k}}{d_{k}*\upsilon_{k}} \right)*\left( \frac{\Delta\quad x_{k}}{d_{k}*\upsilon_{k}} \right)}} \\{\sum{\left( \frac{{- \Delta}\quad y_{k}}{d_{k}*\upsilon_{k}} \right)*\left( \frac{\Delta\quad x_{k}}{d_{k}*\upsilon_{k}} \right)}} & {\sum\left( \frac{\Delta\quad x_{k}}{d_{k}*\upsilon_{k}} \right)^{2}}\end{bmatrix}^{- 1} = \begin{bmatrix}\sigma_{x}^{2} & p_{12} \\p_{21} & \sigma_{y}^{2}\end{bmatrix}}$

-   -   where: Δγ_(k)=vertical component of distance from position to        site k.    -   Δx_(k)=horizontal component of distance from position to site k.    -   d_(k)=distance to site k.    -   υ_(k)=variance of measurement k.

For TDOA, the covariance matrix is: $P = {\begin{bmatrix}{\sum\left( \frac{\Delta\quad x_{k}}{d_{k}*\upsilon_{k}} \right)^{2}} & {\sum{\left( \frac{\Delta\quad y_{k}}{d_{k}*\upsilon_{k}} \right)*\left( \frac{\Delta\quad x_{k}}{d_{k}*\upsilon_{k}} \right)}} & {\sum\left( \frac{\Delta\quad x_{k}}{d_{k}*\upsilon_{k}} \right)} \\{\sum{\left( \frac{\Delta\quad y_{k}}{d_{k}*\upsilon_{k}} \right)*\left( \frac{\Delta\quad x_{k}}{d_{k}*\upsilon_{k}} \right)}} & {\sum\left( \frac{\Delta\quad y_{k}}{d_{k}*\upsilon_{k}} \right)^{2}} & {\sum\left( \frac{\Delta\quad y_{k}}{d_{k}*\upsilon_{k}} \right)} \\{\sum\left( \frac{\Delta\quad x_{k}}{d_{k}*\upsilon_{k}} \right)} & {\sum\left( \frac{\Delta\quad y_{k}}{d_{k}*\upsilon_{k}} \right)} & {\sum\left( \frac{1}{\upsilon_{k}} \right)}\end{bmatrix}^{- 1} = \left\lbrack \quad\begin{matrix}\sigma_{x}^{2} & p_{12} & p_{13} \\p_{21} & \sigma_{y}^{2} & p_{23} \\p_{31} & p_{32} & \sigma_{b}^{2}\end{matrix}\quad \right\rbrack}$

-   -   where: Δγ_(k)=vertical component of distance from position to        site k.    -   Δx_(k)=horizontal component of distance from position to site k.    -   d_(k)=distance to site k.    -   υ_(k)=variance of measurement k.

For combined AOA-TDOA, the first matrix is added to the upper left rowsand columns of the second matrix and then the resulting matrix isinverted to yield the desired covariance matrix.

In all three cases, the semi-major axis of the error ellipse is:$\sigma = \sqrt{\frac{1}{2}\left\lbrack {\sigma_{x}^{2} + \sigma_{y}^{2} + \sqrt{\left( {\sigma_{x}^{2} - \sigma_{y}^{2}} \right)^{2} + {4P_{12}^{2}}}} \right\rbrack}$

The error results are then output in the form of a display map coveringthe cluster or metropolitan area. Color-coding is keyed to the size ofthe estimated error. An example of this output is shown in FIG. 13.Alternately, the estimated probability that the error will meet aspecified criterion is displayed. A host of intermediate results such asforward and reverse link margins and Cellular best server can also bedisplayed in support of location system planning activities. Anothervery useful output plot is the number of location sensors “seeing”;i.e., receiving a useful signal above sensitivity floor, from a handset.An example of this type of plot is shown in FIG. 14. Outputs as thoseshown in FIG. 13 and FIG. 14 provide clear graphical representations oflocation system accuracy and availability.

Default outputs in many cases are graphical; e.g., location errorcontours, GDOP contours, coverage areas, color coded regions to indicatethe number of observing receivers above a certain threshold., and so on.However, the user has the option to display certain outputs in otherformats, e.g., tables.

Printing dialogue windows are user friendly, permitting the user to useboth map scale specifications as well as mouse movements to select theprintable area. Different icons are used to signify different sitecategories. For example, existing, proposed, what-if, and neighboringare possible categories that have somewhat different icons to assist theuser in the analysis.

The user is able to select the colors for the color-coded displayedcategories through dialogue windows under an “Options” menu entry.Preferably, certain color components, e.g., terrain shading gradations,water bodies, morphology categories, highways, etc., may be availablefor selection by the user. The graphical outputs are of sufficientlyhigh resolution and the refresh speed of the screen is maintained highenough to provide the user with a good work environment.

Moreover, the tool supports common business-quality inkjet colorprinters. Varying paper sizes are supported by the tool as well. Blackand white report and table printing are also supported. In oneembodiment, printing control is performed through menu selection.Printer selection and feature control are provided through printer setupdialogue windows. Print item or area selection are provided throughdialogue windows. Both keyboard entry of print object size as well asmouse-based specification of an area on the display are possible. Bothdirect connection to the printer and connection through networks such asa LAN are supported.

The tool also supports interfacing to certain outside systems. It isconvenient at times to import database information from outside sources.This, at times, is the only way a database can be maintained currentwith a dynamic deployment. For example, the cell site database may bemaintained in the mobile switching center (MSC) or connected to it andhas up to date information on the wireless systems' cells, channelassignments, powers, etc. This information may also be imported by thetool.

The tool also provides data base and system security. Preferably, usercreated data used in an analysis session cannot be deleted except by itsowner or by the system administrator. Also, a user may save the dataused in a session for subsequent use. The system includes passwordaccess control for using the tool.

With its user friendly GUI, structured menus, and various intermediateand final output options, the tool is a flexible, interactive tool thatoffers the wireless location system planner a host of powerful designcapabilities. Not only does it enable the user to determine locationsystem performance, it also enables him or her to conduct exercises tooptimize location site placement, and to perform various coverage andcost-benefit tradeoffs.

It will be recognized by those skilled in the art that variousmodifications may be made to the illustrated and other embodiments ofthe invention described above, without departing from the broadinventive scope thereof. It will be understood therefore that theinvention is not limited to the particular embodiments or arrangementsdisclosed, but is rather intended to cover any changes, adaptations ormodifications which are within the scope and spirit of the invention asdefined by the appended claims.

1. A method for analyzing performance of a wireless location systemcomprising: storing data related to location equipment, wirelessinfrastructure, handsets and terrain; generating a site file forpredicting path loss and predicting time/angle error based on the storedterrain data; computing multi-site forward and multi-site reverse linksignal strengths for determining coverage of the location system;generating multi-site margin data and multi-site delay/angle error datafrom the computed multi-site forward and reverse link signal strengthsand the stored data; and generating location error estimates for thewireless location system from the predicted delay/angle error data. 2.The method of claim 1, further comprising displaying the generated errorestimates in a map.
 3. The method of claim 1, further comprising storingmorphology data.
 4. The method of claim 1, wherein the step ofgenerating a site file comprises: computing a propagation predictionmodel to generate a path loss from a mobile unit to each of a pluralityof receiving sites; computing loss due to antenna pattern; and computingtime/angle errors from the mobile unit to each of the plurality ofreceiving sites.
 5. The method of claim 4, wherein the site file is aradial site file and the path loss includes effects of diffraction andantenna height at each point along each radial in the radial site file.6. The method of claim 4, wherein the margin data comprises a margin mapand the multi-site delay/angle error data comprises a multi-sitedelay/angle error map, and wherein the generating location errorestimates comprises generating a location error estimate map for thewireless location system from covariance at each point in the margin mapand the multi-site delay/angle error data.
 7. The method of claim 1,wherein the site file for path loss includes at each point path loss forthe best wireless server, and path loss and error data for all siteswith received signals.
 8. The method of claim 1, further comprisingconverting the generated site file to a cluster map for path loss andtime/angle error.
 9. The method of claim 1, wherein the step ofcomputing multi-site forward and reverse link signal strengthscomprises: invoking stored terrain data; selecting a stored propagationprediction model from a plurality of stored propagation predictionmodels; computing a forward link propagation loss using the selectedpropagation prediction model; and determining a likely server for agiven location.
 10. The method of claim 1, further comprising: using amobile unit power control window and an estimate of received signalstrength on the reverse link for setting a mobile unit's transmit power;generating the mobile unit Tx power map; and using the generated mobileunit Tx power map for generating a multi-site RX power map.
 11. Themethod of claim 1, wherein the step of computing a multi-site forwardand a reverse link signal strength map comprises selecting a propagationprediction algorithm from a plurality of stored propagation predictionalgorithms, wherein inputs to the selected propagation predictionalgorithm includes one or more of terrain data, location and heights ofmobile receiver; location and heights of fixed receiver, land use, majorroad structures, and peculiar obstacles defined in the area.
 12. Themethod of claim 1, further comprising reading, maintaining, anddisplaying one or more of interstate roads, major roads, and secondaryroads.
 13. The method of claim 1, further comprising performingsensitivity analysis by adjusting a parameter.
 14. The method of claim1, further comprising the steps of generating an output in form of oneor more of average errors, RMS errors, number and identity of locationreceivers, and coverage availability.
 15. The method of claim 1, furthercomprising the step of storing in a database information specific to alocation technology including one or more of type of technology; antennatypes; receiver sensitivity data; receiver noise data; receiverbandwidth; integration time; known receiver biases; receiver jitter;transfer function; presence of quality indicators at receiver orreceiver type; and quality indicators computation.
 16. A system forperformance analysis of a location system comprising: means for storingdata related to location equipment, wireless infrastructure, handsetsand terrain; means for generating a site file for predicting path lossand predicting time/angle error based on the stored terrain data; meansfor computing multi-site forward and multi-site reverse link signalstrengths for determining coverage of the location system; means forgenerating multi-site margin data and multi-site delay/angle error datafrom the computed multi-site forward and reverse link signal strengthsand the stored data; and means for generating location error estimatesfor the wireless location system from the predicted delay/angle errordata.
 17. The system of claim 16, wherein the means for generating asite file comprises: means for computing a propagation prediction modelto generate a path loss from a mobile unit to each of a plurality ofreceiving sites; means for computing loss due to antenna pattern; andmeans for computing time/angle errors from the mobile unit to each ofthe plurality of receiving sites.
 18. The system of claim 17, whereinthe site file is a radial site file and the path loss includes effectsof diffraction and antenna height at each point along each radial in theradial site file.
 19. The system of claim 17, wherein the margin datacomprises a margin map and the multi-site delay/angle error datacomprises a multi-site delay/angle error map, and wherein the generatinglocation error estimates comprises generating a location error estimatemap for the wireless location system from covariance at each point inthe margin map and the multi-site delay/angle error data.
 20. A methodfor analyzing performance of a wireless location system comprising:storing data related to location equipment, wireless infrastructure,handsets and terrain; generating a radial model and a radial mapincluding a plurality of radial paths for a site from stored data;selecting a propagation model from a stored plurality of propagationmodels for predicting a path loss along each radial path; predictingaccumulated angular errors and time delay errors at each point along aradial path in the radial map; and generating an error estimate from theaccumulated angular errors and the time delay errors due to terrain andmorphology.